Edit ‘the_seventy_maxims_of_maximally_effective_machine_learning_engineers’
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@@ -24,7 +24,7 @@ Based on [[https://schlockmercenary.fandom.com/wiki/The_Seventy_Maxims_of_Maxima
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*. If you’re manually labeling data, somebody’s done something wrong.
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*. Memory-bound and compute-bound should be easier to tell apart.
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*. Any sufficiently advanced algorithm is indistinguishable from a matrix multiplication.
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*. If your model’s failure is covered by the SLA, you didn’t test enough edge cases.
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*. If your kernel obeys the hardware manufacturer's documentation, you didn't do enough optimization.
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*. “Fire-and-forget training” is fine, provided you never actually forget to monitor the run.
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*. Don’t be afraid to be the first to try a random seed.
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*. If the cost of cloud compute is high enough, you might get promoted for shutting down idle instances.
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